Table REF  compares BCNet with state-of-the-art instance segmentation methods on COCO dataset.
BCGN achieves consistent improvement on different backbones and object detectors, demonstrating its effectiveness by outperforming both PANet [1]} and Mask Scoring R-CNN [2]} by 1.5 AP using Faster R-CNN, and exceeding CenterMask [3]} by 1.3 AP using FCOS. Our single model achieves comparable result with HTC [4]}, which uses a 3-stage cascade refinement with multiple object detectors and mask heads, and far more parameters.
